Predicting missing links in global host‐parasite networks

Using phylogeographic link-prediction in primates to prioritize human parasite screening

Objectives

The ongoing risk of emerging infectious disease has renewed calls for understanding the origins of zoonoses and identifying future zoonotic disease threats. Given their close phylogenetic relatedness and geographic overlap with humans, non-human primates (NHPs) have been the source of many infectious diseases throughout human evolution. NHPs harbor diverse parasites, with some infecting only a single host species while others infect species from multiple families.

Materials and Methods

We applied a novel link-prediction method to predict undocumented instances of parasite sharing between humans and NHPs. Our model makes predictions based on phylogenetic distances and geographic overlap among NHPs and humans in six countries with high NHP diversity: Columbia, Brazil, Democratic Republic of Congo, Madagascar, China and Indonesia.

Results

Of the 899 human parasites documented in the Global Infectious Diseases and Epidemiology Network (GIDEON) database for these countries, 12% were shared with at least one other NHP species. The link prediction model identified an additional 54 parasites that are likely to infect humans but were not reported in GIDEON. These parasites were mostly host generalists, yet their phylogenetic host breadth varied substantially.

Discussion

As human activities and populations encroach on NHP habitats, opportunities for parasite sharing between human and non-human primates will continue to increase. Our study identifies specific infectious organisms to monitor in countries with high NHP diversity, while the comparative analysis of host generalism, parasite taxonomy, and transmission mode provides insights to types of parasites that represent high zoonotic risk.

Network embedding unveils the hidden interactions in the mammalian virome

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.

Mussel parasite richness and risk of extinction

Parasite conservation is important for the maintenance of ecosystem diversity and function. Conserving parasites relies first on understanding parasite biodiversity and second on estimating the extinction risk to that biodiversity. Although steps have been taken independently in both these areas, previous studies have overwhelmingly focused on helminths in vertebrate hosts over broad scales, providing low resolution and excluding a large proportion of possible host and parasite diversity. We estimated both total obligate parasite richness and parasite extinction risk in freshwater mussels (Unionidae and Margaritiferidae) from Europe and the United States to provide a case study for considering parasite conservation in a severely understudied system. We used currently reported host-parasite relationships to extrapolate parasite diversity to all possible mussel hosts and then used the threat levels of those hosts to estimate the extinction risk for both described and undescribed parasites. An estimated 67% of parasite richness in freshwater mussels is undescribed and over 80% of the most host-specific groups (digenean trematodes and ciliates) are undescribed. We estimated that 21% of this total parasite fauna is at immediate risk of extinction, corresponding to 60 unique species, many of which will likely go extinct before being described. Given the important roles parasites play in community structure and function and the strong ecosystem engineering capacities of freshwater mussels, such extinctions are likely to severely affect freshwater ecosystems. Our detailed study of mussel parasites provides compelling evidence for the hidden conservation threat to parasites through extinction cascades and shows parasites are deserving of immediate attention.

Fish-parasite interaction networks reveal latitudinal and taxonomic trends in the structure of host-parasite associations

In recent years, treating host–parasite associations as bipartite interaction networks has proven a powerful tool to identify structural patterns and their likely causes in communities of fish and their parasites. Network analysis allows for both community-level properties to be computed and investigated, and species-level roles to be determined. Here, using data from 31 host–parasite interaction networks from local fish communities around the world, we test for latitudinal trends at whole-network level, and taxonomic patterns at individual parasite species level. We found that while controlling for network size (number of species per network), network modularity, or the tendency for the network to be subdivided into groups of species that interact mostly with each other, decreased with increasing latitude. This suggests that tropical fish–parasite networks may be more stable than those from temperate regions in the event of community perturbations, such as species extinction. At the species level, after accounting for the effect of host specificity, we observed no difference in the centrality of parasite species within networks between parasites with different transmission modes. However, species in some taxa, namely branchiurans, acanthocephalans and larval trematodes, generally had higher centrality values than other parasite taxa. Because species with a central position often serve as module connectors, these 3 taxa may play a key role in whole-network cohesion. Our results highlight the usefulness of network analysis to reveal the aspects of fish–parasite community interactions that would otherwise remain hidden and advance our understanding of their evolution.